Map matching is a process that matches location data (e.g., GPS data) received from a device with a proposed road link on which the device may have traveled. When the location data is sampled over time, map matching can help to reconstruct the travel path taken by the device through a road or other transportation network. Accordingly, map matching generally is a fundamental first step in processing probe data (e.g., location trace data) for many applications—such as traffic analytics, route analytics, behavioral analytics, and the like. In many cases, measurement error and sampling frequency can cause significant difficulties in reconstructing the path that the device took from the probe data. While implementing a high sampling frequency can solve some of these map matching difficulties, it can also result in high battery or power usage over time, particularly in mobile devices such as phones, personal navigation devices (PNDs), Internet-of-things (IoT) devices, and the like. In addition, a sampling frequency that is set too high also can result in collecting, storing, and/or processing large amounts of potentially unnecessary location data. The problem of data overabundance can be further exacerbated when thousands or even millions of devices are collecting probe or location trace data.